Algebraic Dead Directions in LayerNorm Transformers: A Forward-Pass-Only Diagnostic at LLM Scale
arXiv:2606.19491v1 Announce Type: new Abstract: Pretrained transformers sit near singular minima of the loss, where the Fisher information metric degenerates along dead directions: directions in parameter space along which the directional Fisher vanishes. Locating such a direction normally needs a forward pass and an eigendecomposition of activations, or a sampling-based complexity estimate; none returns a direction computable from the network's parameters alone. We give one, for LayerNorm transformers. The inverse-scale direction $\gamma^{-1}/\|\gamma^{-1}\|$ of the LayerNorm affine is an exa
The paper provides a novel diagnostic tool for understanding the stability and efficiency of large language models, addressing a critical technical challenge in their continued scaling and deployment.
This research offers a method to identify 'dead directions' in LayerNorm transformers using only network parameters, potentially leading to more stable, efficient, and robust AI models.
The ability to diagnose model 'dead directions' without resource-intensive activation eigendecomposition or sampling-based estimates changes how researchers debug and optimize LLMs.
- · AI Researchers
- · Large Language Model Developers
- · Cloud Computing Providers
- · Inefficient AI Model Designs
- · High-Cost Diagnostic Methods
More efficient and reliable methods for AI model development and optimization become available.
This leads to faster iteration and deployment of increasingly complex and capable AI systems in a variety of sectors.
Improved model stability and efficiency could reduce the computational overhead for AI, addressing aspects of the energy bottleneck narrative.
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Read at arXiv cs.LG